Earlier this year, we did something unusual: we locked our system architects in a room for three months and let them rebuild the platform from the ground up. They came out with a new architecture and an engineering organization that looks nothing like the one that went in.

The result isn't a moderate improvement. We've multiplied our team's delivery capacity many times over, and it's been one of the most exciting journeys in Casi's history. This is the first article in a series about that journey: from struggling to get real value out of AI, to making our platform AI-ready, to where we are now, producing more than we ever thought possible.

From 30+ repositories to one

The change at the core: migrating from more than 30 microservices spread across separate repositories into a single shared codebase, a monorepo. Not because the old architecture was broken, but because the way software gets built has fundamentally changed. An AI coding agent is only as good as the context it can see. Spread across 30+ repositories, no agent, and frankly no single human, could hold the full picture of how our e-commerce layer, contracts, billing, and fleet operations fit together.

We're not alone in reaching this conclusion. Across the industry, 2026 is being called the year of the monorepo, precisely because AI agents work dramatically better when they can read an entire system in one go. Airbnb famously compressed an 18-month migration into six weeks using AI agents working inside a monorepo.

The problem flipped

Before, our constraint was straightforward: we couldn't write code fast enough to keep pace with what the business needed. That constraint is gone. If anything, the opposite is now true, we write more code than we can humanly keep up with.

"Coding is no longer our problem, if anything, we code too much. We've already had to stop doing human code review the old way. We've introduced a new step in our delivery flow instead: robots write the code, robots review the code, and human input comes in right at the end."

Gunnar Birkenfeldt, Chief Product Officer

That shape isn't as radical as it sounds. Enterprises modernizing far bigger, older systems than ours are landing on the same idea: don't remove human judgment, relocate it toward architecture and the final gate before something ships. We just got to design it in from scratch.

Data is the oxygen

A shared codebase makes AI agents fast, but speed alone isn't the point. Coding is the easy problem. Understanding a business well enough to act on it is the hard one, and that takes clean, structured data, not just clean code.

"Data is the oxygen of our platform. AI is only as good as the domain data we feed it, clean, structured, and in context. That combination, not coding speed, is where the real magic happens."

Gunnar Birkenfeldt, Chief Product Officer

It's the same pattern research keeps finding across the industry: companies that build AI around a structured data model from day one pull ahead of the ones bolting AI onto whatever data they already had. For big, older organizations, data quality, not model capability, is consistently named as the real obstacle to getting value from AI.

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Everyone closest to the problem is now a builder

With one codebase, one data model, and AI agents on top of both, building is no longer reserved for engineers. Our business and finance colleagues, the people with the deepest domain knowledge of residual values, invoice flows, and fleet economics, are building prototypes themselves, and in many cases shipping new functionality straight into the platform.

The person who understands a problem best can now express the solution directly, with AI agents doing the translation into working software. Humans still have the final say, that's the last step in the new flow, but the distance from insight to shipped functionality has never been shorter.

What's next

This migration was phase one: making the platform AI-ready, architecturally and in how our data is structured. The next phase is AI implementation, putting these superpowers to work across the business. We'll share concrete examples in the next articles in this series, including Casi Apex, which turns our data into insight for the customers who rely on us, and our new vehicle ledger, which will give a transparent unit economy per vehicle. We're also excited to explore how this same building power extends beyond our own team, to the people on the customer side who understand their own business best.

For our customers, OEMs, FleetCos, and finance companies, the throughline is simple: new capabilities ship faster, built by the people who understand your business best, on top of data clean enough for AI to actually reason over.

Want to hear more about how we made our platform AI-ready? Reach out, we're happy to share what we learned along the way.